Application of machine learning algorithms inprediction of hotel booking cancellation
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Machine learning algorithms have been applied in various fields. In hotel booking, machine learning algorithms have shown promising results. The applications of machine learning algorithms in prediction of hotel booking cancellation have attracted much attention. The capability of machine learning plays an important role to predict hotel booking cancellation early and accurately. The paper presents the applications of various machine learning algorithms of prediction of hotel booking cancellation. The performance evaluation on a large public dataset has shown the applicable results of the machine learning applications.
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1. M.Jishan et al. Hotel Booking Cancellation Prediction Using Applied Bayesian Models. 3 2024 International Conference on Decision Aid Sciences and Applications (DASA), 2024, doi: 110.1109/DASA63652.2024.10836282.
2. N.A.Putro et al., PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM, Jurnal Techno Nusa Mandiri 18(1):1-8, 2021.
3. Y.-M. Chang, C.-H. Chen, J.-P. Lai, Y.-L. Lin, and P.-F. Pai, “Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews”, Applied Sciences, vol. 11, no. 21, p. 10291, 2021.
4. E. C. Sánchez, A. J. Sánchez-Medina, and M. Pellejero, “Identifying critical hotel cancellations using artificial intelligence”, Tourism Management Perspectives, vol. 35, p. 100718, 2020.
5. M. A. Afrianto and M. Wasesa, “Booking Prediction Models for Peerto-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers”, Journal of Information Systems Engineering and Business Intelligence, vol. 6, no. 2, pp. 123–132, 2020.
6. J. Sánchez-Medina and E. C.-Sánchez, “Using machine learning and big data for efficient forecasting of hotel booking cancellations”, International Journal of Hospitality Management, vol. 89, p. 102546, 2020.
7. M. S. Satu, K. Ahammed, and M. Z. Abedin, “Performance Analysis of Machine Learning Techniques to Predict Hotel Booking Cancellations in Hospitality Industry”, in 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp. 1–6, 2020.
8. Antonio, A. de Almeida, and L. Nunes, “Big data in hotel revenue management: Exploring cancellation drivers to gain insights into booking cancellation behavior”, Cornell Hospitality Quarterly, vol. 60, no. 4, pp. 298–319, 2019.
9. D. Romero Morales and J. Wang, “Forecasting cancellation rates for services booking revenue management using data mining”, European Journal of Operational Research, vol. 202, no. 2, pp. 554-562, 2010.
10. Herrera, A. Arroyo, A. Jimenez, and Á. Herrero, “Forecasting hotel cancellations through machine learning”, Expert Systems, 2024.
11. Rice Leaf Diseases Dataset, https://www.kaggle.com/datasets/dedeikhsandwisaputra/riceleafs-disease-dataset, Accessed: 2025-04-20.
12. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer. ISBN: 978-1-4614-7137-0.
13. Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. DOI: 10.1007/BF00994018.
14. Breiman, L., Friedman, J., Olshen, R., Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0412048418.
15. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32, DOI:10.1023/A:1010933404324.
Copyright (c) 2025 Bui Hai Phong, Nguyen Le Phuong Vy, Trieu Ngoc Van, Nguyen Viet Nhat Minh, Nguyen Ngoc Thanh, Trinh Nam Khanh, Ngo Nam Khanh
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